Business Intelligence (BI) generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information, content delivery infrastructure systems for delivery and management of reports and analytics, data warehousing systems for cleansing and consolidating information from disparate sources, and data management systems, such as relational databases or On Line Analytic Processing (OLAP) systems used to collect, store, and manage raw data.
OLAP tools are a subset of business intelligence tools. There are a number of commercially available OLAP tools including Business Objects Voyager™ which is available from Business Objects Americas of San Jose, Calif. An OLAP tool is a report generation tool that is configured for ad hoc analyses. OLAP generally refers to a technique of providing fast analysis of shared information stored in a multidimensional database. OLAP systems provide a multidimensional conceptual view of data, including full support for hierarchies and multiple hierarchies. This framework is used because it is a logical way to analyze businesses and organizations. In some OLAP tools the data is arranged in a schema which simulates a multidimensional schema. The multidimensional schema means redundant information is stored, but it allows for users to initiate queries without the need to know how the data is organized.
There are other report generation tools, including tools that couple to a metadata layer that overlies a data source. The metadata layer can be a semantic metadata layer, or semantic layer, which includes metadata about the type of data within the data source. Some metadata layers map the data source fields into familiar terms, such as, product, customer, or revenue. The metadata layer can provide a multidimensional view of information in a data source. There are a number of commercially available report generation tools that are characterized by a semantic layer, including Business Objects Web Intelligence™, which is available from Business Objects Americas of San Jose, Calif.
There are known techniques for graphically portraying quantitative information. The techniques are used in the fields of statistical graphics, data visualization, and the like. Charts, tables, and maps are visualizations of quantitative information. Visualizations are produced from data in a data source (e.g., an OLAP cube, relational database). A visualization is a graphic display of quantitative information. Types of visualizations include charts, tables, and maps. Visualizations can reveal insights into the relationships between data. The data within an OLAP cube may be comprised of categorical dimensions, numerical measure dimensions, and time dimensions. A categorical dimension is a data element that categorizes each item in a data set into non-overlapping regions. A numerical measure dimension comprises data defined by a computation, such as a sum or average. For example, an OLAP cube of Beverages might have categorical dimensions such as Product, Country, Color, Volume, Alcohol Level, and Sweetness and numerical measures such as Revenue and Profit margin. The time dimension comprises data grouped in accordance with a time metric. For example, time dimensions may include Quarter 1, Quarter 2, Quarter 3, and Quarter 4. Multidimensional databases undertake to provide fast navigation and informative presentation of data inside an OLAP cube.
However, existing multidimensional databases have limitations with regards to their ability to deliver these results. Existing multidimensional databases are user driven, giving little direction into effective navigation of the data therein. The problem has been further augmented as the data volumes within OLAP cubes have increased and forced data navigation to become even more complex.
In view of the foregoing, it would be highly desirable to provide an improved technique for guided navigation through the data within an OLAP cube. In particular, it would be highly desirable to provide a method for guided graphical navigation through the categorical, numerical measures, and time dimensions of an OLAP cube.